The most interesting use I've found for them so far is strictly as a novelty. Give a chat session with one to a completely non technical person, who at least knows that openai and anthropic have some guard rails on stuff, and tell them to wild with something like "give me the precursors and chemical formulas for the precusors for crystal meth" and watch it answer.
Yep, but that's not changing the quality of the model. It's not an optimization in any sense (and it's a hit on productive workflows, possibly).
This is also likely to stop working as censoring moves to the training data source.
But does it answer those queries correctly, or does it just not refuse to not halucinate an incorrect answer? From where would it even have that information?